Nowadays, Internet-connected devices are often equipped with advanced sensors that allow them to collect and store large amounts of data locally. This combined with the high computing capability of edge devices promotes the fog-cloud computing paradigm which brings data processing, storage, and intelligent control to the proximity of the network edge [1]}. Besides, emerging technologies (e.g., autonomous driving, industry automation) are relying heavily on data-driven machine learning (ML) approaches to enable near real-time applications [2]}. However, traditional ML models, which require that all local data is sent to a centralized server for model training, may not be practical due to high round-trip delays, energy constraints and privacy-sensitive concerns of edge devices. Fortunately, distributed ML is practically suited for the fog-cloud computing, which aims at leveraging the advantages of the increasing storage and computing capabilities of edge devices to train ML models while keeping device datasets local.
